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Marek Perkowski INTRODUCTION TO MODAL AND EPISTEMIC LOGIC for beginners.

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1 Marek Perkowski INTRODUCTION TO MODAL AND EPISTEMIC LOGIC for beginners

2 Overview 1.Why we need moral robots 2.Review of classical Logic 3.The Muddy Children Logic Puzzle 4.The partition model of knowledge 5.Introduction to modal logic 6.The S5 axioms 7.Common knowledge 8.Applications to robotics 9.Knowledge and belief

3 The goals of this series of lectures innovative research My goal is to teach you everything about modal logic, deontic logic, temporal logic, proof methods etc that can be used in the following areas of innovative research: 1.Robot morality (assistive robots, medical robots, military robots) 2.Natural language processing (robot assistant, robot-receptionist) 3.Mobile robot path planning (in difficult “game like” dynamically changeable environments) 4.General planning, scheduling and allocation (many practical problems in logistics, industrial, military and other areas) 5.Hardware and software verification (of Verilog or VHDL codes) 6.Verifying laws and sets of rules (like consistency of divorce laws in Poland) 7.Analytic philosophy (like proving God’ Existence, free will, the problem of evil, etc) 8.Many other… At this point I should ask all students to give another examples of similar problems that they want to solve

4 Big hopes Modal logic is a very hot topic in recent ~12 years. In my memory I see new and new areas that are taken over by modal logic Let us hope to find more applications Every problem that was formulated or not previously in classical logic, Bayesian logic, Hiden Markov models, automata, etc can be now rewritten to modal logic.

5 The Tokyo University of Science: Saya Morality for non-military robots that deal directly with humans. At this point I should ask all students to give another examples of dialogs, that would include reasoning, to have with Saya

6 6 MechaDroyd Typ C3 Business Design, Japan At this point I should ask all students to give another examples of dialogs, that would include reasoning, to have with MechaDroyd

7 My Spoon – Secom in Japan

8 My Spoon – Secom 1.At this point I should ask all students to give examples of robots to help elderly, autistic children or handicapped and what kind of morality or deep knowledge this robot should have. 2.Example, a robot for old woman, 95 years old who cannot find anything on internet and is interested in fashion and gossip.

9 Robot IPS

10 Fuji Heavy Industries/Sumitomo Cleaning lawn- moving, and similar robots will have contact with humans and should be completely safe What kind of deep knowledge and morality a robot should have for standard large US hospital?

11 Care robot 1.How much trust you need to be in arms of a strong big robot like this? 2.How to build this trust? 3.What kind morality you would expect from this robot? R. Capurro: Cybernics Salon

12 Robots and War 1.Congress: one-third of all combat vehicles to be robots by Future Combat System (FCS) Development cost by 2014: $130-$250 billion

13 Robotex (Palo Alto, California) by Terry Izumi We urgently need robot morality for military robots It is expected that these robots will be more moral than contemporary US soldiers in case of accidental shootings of civilians, avoiding panic behaviors, etc

14

15 Review of classical logic

16 Classical logic What is logic? – A set of techniques for representing, transforming, and using information. What is classical logic? – A particular kind of logic that has been well understood since ancient times. – (Details to follow…)

17 Classical vs non-classcial logic I should warn you that non-classical logic is not as weird as you may think. – I’m not going to introduce “new ways of thinking” that lead to bizarre beliefs. – What I want to do is make explicit some non- classical ways of reasoning that people have always found useful. I will be presenting well-accepted research results, not anything novel or controversial.

18 Classical logic in Ancient times 300s B.C.: ARISTOTLE and other Greek philosophers discover that some methods of reasoning are truth- preserving. That is, if the premises are true, the conclusion is guaranteed true, regardless of what the premises are.

19 Example of Classical logic Syllogism All hedgehogs are spiny. Matilda is a hedgehog. ∴ Matilda is spiny. You do not have to know the meanings of “hedgehog” or “spiny” or know anything about Matilda in order to know that this is a valid argument.

20 What Classical logic can really do? VALID means TRUTH-PRESERVING. Logic cannot tell us whether the premises are true. The most that logic can do is tell us that IF the premises are true, THEN the conclusions must also be true.

21 Classical logic since : George Boole points out that inferences can be represented as formulas and there is an infinite number of valid inference schemas. ( ∀ x) hedgehog(x) ⊃ spiny(x) hedgehog(Matilda) ∴ spiny(Matilda) Proving theorems (i.e., proving inferences valid) manipulating formulas is done by manipulating formulas.

22 What is an argument? An argument is any set of statements one of which, the conclusion, is supposed to be epistemically supported by the remaining statements, the premises.

23 Is this an argument? 1.Ms. Malaprop left her house this morning. 2.Whenever she does this, it rains. _____________ 3.Therefore, the moon is made of blue cheese.

24 What is a good argument? An argument is valid if and only if the conclusion must be true, given the truth of the premises.

25 Is this argument valid? 1.If the moon is made of blue cheese, then pigs fly. 2.The moon is made of blue cheese. ______________ 3.Therefore, pigs fly.

26 What we aim for An argument is sound if and only if the argument is valid and, in addition, all of its premises are true.

27 Logical Negation Consider the following sentences “2 plus 2 is 4” is true “2 plus 2 isn’t 4” is false “2 plus 2 is 5” is false “2 plus 2 isn’t 5” is true

28 Truth Table for Negation pnot p True (T)False (F) True (T)

29 Definition of Logical Negation A sentence of the form “not-p” is true if and only if p is false; otherwise, it is false. So logically speaking negation has the effect of switching the truth-value of any sentence in which it occurs.

30 Other English phrases That claim is irrelevant Your work is unsatisfactory It is not true that I goofed off all summer.

31 Logical Connectives Technically, negation is a one-place logical connective, meaning that negation combines with a single sentence to produce a more complex sentence having the opposite truth- value as the original.

32 The Material Conditional The material conditional is most naturally represented by the English phrase “if …, then...”. For example, “If you study hard in this class, you will do well.”

33 Truth Table for the Conditional pqIf p, then q TTT TFF FTT FFT

34 Definition of the Conditional A sentence of the form “if p, then q” is true if and only if either p is false or q is true; otherwise it is false.

35 Some More Jargon Technically, “if …, then …” is a two-place sentential connective : it takes two simpler sentences and connects them into a single, more complex sentence. The first sentence p is called the antecedent. The second sentence q is called the consequent.

36 Conjunction Which of the following are true? = 4 and = = 5 and = = 4 and = = 5 and = 8.

37 Truth Table for Conjunction pqp and q TTT TFF FTF FFF

38 Definition of Conjunction A sentence of the form “p and q” is true if and only if p is true and q is true; otherwise it is false. The two sentences p and q are known as the conjuncts.

39 Inclusive “Or” (Disjunction) Example Marion Jones is worried that she is not going to win a medal in the ’04 Olympic games. Her husband assures her that she will surely place in one of the two events she has qualified for: ‘It’s ok,’ he says. “Either you’ll medal in the long jump or in the 400m relay.”

40 Truth Table for Disjunction pqp or q TTT TFT FTT FFF

41 Definition of Disjunction A sentence of the form “p or q” is true if and only if either p is true or q is true or both p and q are true; otherwise, it is is false. The two sentences p and q are knows as the disjuncts.

42 Exclusive “Or” Suppose your waiter tells you that you can have either rice pilaf or baked potato with your dinner. In such circumstances, he plainly does not mean either rice pilaf or baked potato or both. You have to choose. So this use of “or” doesn’t fit the definition of disjunction given above.

43 Defining Exclusive “Or” Rather than introducing exclusive “or” via a truth table, logicians usually just define it in terms of negation, conjunction and disjunction: (p or q) and not-(p and q)

44 Material Biconditional 1.Sometimes we want to say that two sentences are equivalent ―that is they are both true or false together. 2.For instance, I might tell you that John is a bachelor if and only if John is an unmarried adult male.

45 Truth Table for the Biconditional pq p if and only if q TTT TFF FTF FFT

46 Defining the Biconditional A sentence of the form “p if and only if q” is true if and only if either both p and q are true or both p and q are false; otherwise, it is false. Alternatively: p if and only if q = (if p, then q) and (if q, then p)

47 Logic, Knowledge Bases and Agents

48 Syntax versus semantics

49 What does it mean that there are many logics?

50 Types of Logic

51 Review of classical propositional logic Basic concepts, methods and terminology that will be our base in modal and other logics

52 Propositional logic is the logic from ECE 171 Simple and easy to understand Decidable, but NP complete – Very well studied; efficient SAT solvers – if you can reduce your problem to SAT … Drawback – can only model finite domains

53 Truth Table Method and Propositional Proofs

54 You are already used to use Karnaugh maps to interprete all these facts Deduction In deduction, the conclusion is true whenever the premises are true. Premise: p Conclusion: (p  q) Premise: p Non-Conclusion: (p  q) Premises: p, q Conclusion: (p  q)

55 Logical Entailment

56 56 Logical Entailment A set of premises  logically entails a conclusion  (written as  |=  ) if and only if every interpretation that satisfies the premises also satisfies the conclusion. Examples {p} |= (p  q) entails {p} |# (p  q) does not entail {p,q} |= (p  q) entails

57 Comment on defining A set of premises  logically entails a conclusion  (written as  |=  ) if and only if every interpretation that satisfies the premises also satisfies the conclusion. 1.This definition raised some doubt in class 2.But it is only the way how we define things in metalanguage 3.I can write like this A set of premises  logically entails a conclusion  (written as  |=  ) =def= every interpretation that satisfies the premises also satisfies the conclusion.

58 Comment on defining in Meta- Language A set of premises  logically entails a conclusion  (written as  |=  ) if every interpretation that satisfies the premises also satisfies the conclusion. And When every interpretation that satisfies the premises also satisfies the conclusion Then A set of premises  logically entails a conclusion  (written as  |=  ) This is a way of defining

59 59 More on defining A set of premises  logically entails a conclusion  (written as  |=  ) if and only if every interpretation that satisfies the premises also satisfies the conclusion. I am not saying A set of premises  logically entails a conclusion  (written as  |=  ) if and only if every interpretation that satisfies the premises also satisfies the conclusion and every interpretation that satisfies the conclusion also satisfies the premises. If and only if is in definition, this is equality of metalanguage and equality inside the definition. Metalanguage is a different beast!

60 60 Truth Table Method to check entailment 1.We can check for logical entailment by comparing tables of all possible interpretations. 2.In the first table, eliminate all rows that do not satisfy premises. 3.In the second table, eliminate all rows that do not satisfy the conclusion. rows in the first table are a subset 4.If the remaining rows in the first table are a subset of the remaining rows in the second table, then the premises logically entail the conclusion. There are many ways to check entailment. In binary logic it is easy, here is one method. Another is to look to included in set X set S of minterms – ask student.

61 61 Example of using Truth Table method to check entailment Does p logically entail (p  q)?

62 62 Example of using Truth Table method. Other method Does p logically entail (p  q)? 1.In the first table, eliminate all rows that do not satisfy premises. 2.In the second table, eliminate all rows that do not satisfy the conclusion.

63 63 Example of using Truth Table method. Other method Does p logically entail (p  q)? 1.In the first table, eliminate all rows that do not satisfy premises. 2.In the second table, eliminate all rows that do not satisfy the conclusion.

64 One more Example: no entailment. Does p logically entail (p  q)? Does {p,q} logically entail (p  q)?NO Ask a student to show another examples of checking entailment

65 Example If Mary loves Pat, then Mary loves Quincy. If it is Monday, then Mary loves Pat or Quincy. If it is Monday, does Mary love Quincy? mpq 111 11 11 00 101 11 00 00 011 00 11 0 mpq 111  101  Not on Monday Mary does not love Pat and does not love Quincy 1.We can check for logical entailment by comparing tables of all possible interpretations. 2.In the first table, eliminate all rows that do not satisfy premises. 3.In the second table, eliminate all rows that do not satisfy the conclusion. rows in the first table are a subset 4.If the remaining rows in the first table are a subset of the remaining rows in the second table, then the premises logically entail the conclusion. X10 eliminated 100 eliminated It is Monday and Mary does not love Quincy eliminated Yes, Mary Loves Quincy on Monday Conclusion: It is Monday and Mary loves Quincy Is this conclusion true? First variant: Entailment true

66 Example If is always true that if on this day Mary loves Pat, then Mary loves Quincy. If it is Monday, then Mary loves Pat or Quincy. If it is Monday, does Mary love only Pat? mpq 111 11 11 00 101 11 00 00 011 00 11 0 mpq xxx 110 xxx xxx Not on Monday Mary does not love Pat and does not love Quincy 1.We can check for logical entailment by comparing tables of all possible interpretations. 2.In the first table, eliminate all rows that do not satisfy premises. 3.In the second table, eliminate all rows that do not satisfy the conclusion. rows in the first table are a subset 4.If the remaining rows in the first table are a subset of the remaining rows in the second table, then the premises logically entail the conclusion. X10 eliminated 100 eliminated Conclusion: It is Monday and Mary loves only Pat No, statement “Mary Loves only Pat on Monday” is not true Is it true that “It is Monday and Mary loves only Pat” SecondVariant: Entailment not true Mary does not love Pat

67 What did we learn from this example of entailment? 1.As seen in this example, we can formulate many various methods to remove “worlds” (cells of Kmaps, rows of truth tables) from consideration. 2.They can be not described by Boolean formulas but by some other rules of language or behavior. 3.But we can check the entailment by exhaustively checking the relation between minterms of two truth tables, in general by checking some relations directly in the model

68 68 Problem with too many interpretations 1.There can be many, many interpretations for a Propositional Language. 2 n 2.Remember that, for a language with n constants, there are 2 n possible interpretations. 3.Sometimes there are many constants among premises that are irrelevant to the conclusion. Much wasted work. 4.Answer: Proofs 5.Too many interpretations is like extreme Karnaugh maps that you even cannot create

69 69 Patterns A pattern grammatical rules of our language A pattern is a parameterized expression, i.e. an expression satisfying the grammatical rules of our language except for the occurrence of meta-variables (Greek letters) in place of various subparts of the expression. Sample Pattern:   (    ) Instance: p  (q  p) Instance: (p  r)  ((p  q)  (p  r))

70 70 Patterns Questions 1.Is this pattern a tautology, check it using Kmaps or elimination of implication from logic class 2.If I know that this is a tautology, should I check the second instance? Sample Pattern:   (    ) Instance 1: p  (q  p) Instance 2: (p  r)  ((p  q)  (p  r)) Substitute logic variables for formulas

71 Rules of Inference

72 72 Rules of Inference A rule of inference is a rule of reasoning consisting of one set of sentence patterns, called premises, and a second set of sentence patterns, called conclusions.

73 73 Instances of applying rules An instance of a rule of inference is a rule in which all meta-variables have been consistently replaced by expressions in such a way that all premises and conclusions are syntactically legal sentences.

74 74 Four Sound Rules of Inference A rule of inference is sound if and only if the premises in any instance of the rule logically entail the conclusions. Modus Ponens (MP) Modus Tolens (MT) Equivalence Elimination (EE) Double Negation (DN)

75 75 Proof (Version 1) A proof of a conclusion from a set of premises is a sequence of sentences terminating in the conclusion in which each item is either: 1. a premise 2. the result of applying a rule of inference to earlier items in sequence.

76 Example of simple proof When it is raining, the ground is wet. When the ground is wet, it is slippery. It is raining. Prove that it is slippery. At this point I should ask a student to draw the tree of this derivation

77 This is obvious but beware Error Note: Rules of inference apply only to top-level sentences in a proof. Sometimes works but sometimes fails.No!

78 Ask a student to draw the derivation tree Another example of a proof Heads you win. Tails I lose. Suppose the coin comes up tails. Show that you win. Tails is no money

79 Entailment and Models

80 Entailment – Logical Implication This can be found in Kmap, but in real life we cannot create such simple models.

81 1.M(a) some set of ones in a Kmap 2.KB included in it set of cells Models versus Entailment

82 Derivation, Soundness and Completeness It is not so nice for more advanced logic systems We derive alpha from knowledge base

83 Soundness and Completeness in other notation. if, then Soundness: Our proof system is sound, i.e. if the conclusion is provable from the premises, then the premises propositionally entail the conclusion. (  |-  )  (  |=  ) if then Completeness: Our proof system is complete, i.e. if the premises propositionally entail the conclusion, then the conclusion is provable from the premises. (  |=  )  (  |-  ) metalanguage Observe that here we have only if and not iff in metalanguage

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85 Syntax of Propositional Logic

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87 Semantics of Propositional Logic

88 Intuitive explanation what is semantics

89 Formal definition of semantics of propositional logic

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91 Exercise to understand the concept of interpretation 1.Find an interpretation and a formula such that the formula is true in that interpretation – (or: the interpretation satisfies the formula). 2. Find an interpretation and a formula such that the formula is not true in that interpretation – (or: the interpretation does not satisfy the formula). 3. Find a formula which can't be true in any interpretation (or: no interpretation can satisfy the formula).

92 Satisfiability and Validity

93 Definitions of Satisfiability and Validity Ask students to do several exercises

94 Exercises for Satisfiability, Tautology and Equivalency Ask students to do all these exercises with various Kmaps.

95 Consequences of definitions of satisfiability and tautology Important – equivalent formulas can be replaced forward and backward

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97 Enumeration Method – check all possible models

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103 Deduction, Contraposition and Contradiction theorems of propositional logic Can be used in automated theorem proving and reasoning

104 Equivalences of propositional logic

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107 Normal Forms for propositional logic

108 Conjunctive Normal Form and Disjunctive Normal Form SOP POS

109 Conjunction of Horn Clauses Normal Form

110

111 Axiom Schemata

112 112 Axiom Schemata 1.Fact: If a sentence is valid, then it is true under all interpretations. 2.Consequently, there should be a proof without making any assumptions at all. 3.Fact: (p  (q  p)) is a valid sentence. 4.Problem: Prove (p  (q  p)). 5.Solution: We need some rules of inference without premises to get started. axiom schema 6.An axiom schema is sentence pattern construed as a rule of inference without premises.

113 113 Rules and Axiom Schemata Axiom Schemata as Rules of Inference   (    ) Rules of Inference as Axiom Schemata (    )  (    ) Note: 1.Of course, we must keep at least one rule of inference to use the schemata. 2.By convention, we retain Modus Ponens.

114 114 Valid Axiom Schemata A valid axiom schema is a sentence pattern denoting an infinite set of sentences, all of which are valid.   (    )

115 Standard Axiom Schemata II: II:   (    ) ID: ID: (   (    ))  ((    )  (    )) CR: CR: (    )  ((    )   ) (    )  ((    )   ) EQ: EQ: (    )  (    ) (    )  (    ) (    )  ((    )  (    )) OQ: OQ: (    )  (    ) (    )  (    ) (    )   (    )

116 Ask a student to do this without a help from Kmaps Sample Proof using Axiom Schemata 1.Whenever p is true, q is true. 2.Whenever q is true, r is true. 3. Prove that, whenever p is true, r is true.

117 117 Proof (Official Version) A proof of a conclusion from a set of premises is a sequence of sentences terminating in the conclusion in which each item is either: 1.A premise 2.An instance of an axiom schema 3.The result of applying a rule of inference to earlier items in sequence.

118 Observe that if and only if is from definition in metalanguage again Provability provable (written  |-  ) A conclusion is said to be provable from a set of premises (written  |-  ) if and only if there is a finite proof of the conclusion from the premises using only Modus Ponens and the Standard Axiom Schemata. Definition of provable

119 Truth tables versus proofs

120 120 Truth Tables Truth Tables versus Proofs succeed in exactly the same cases. 1.The truth table method and the proof method succeed in exactly the same cases. 2.On large problems, the proof method often takes fewer steps than the truth table method. 3.However, in the worst case, the proof method may take just as many or more steps to find an answer as the truth table method. 4.Usually, proofs are much smaller than the corresponding truth tables. 5.So writing an argument to convince others does not take as much space.

121 Metatheorems of propositional logic Deduction Theorem: if and only if  |- (    ) if and only if  {  } |- . Equivalence Theorem: then  |- (    ) and  |- , then it is the case that  |-  . If some implication is entailed from set delta than the precedence of this formula added to delta entails the consequence of this implication We will show with examples that these theorems are truly useful in proofs

122 122 Without Proof Without Deduction Theorem Problem: {p  q, q  r} |- (p  r)?

123 123 Using Deduction Theorem Proof Using Deduction Theorem Problem: {p  q, q  r} |- (p  r)?

124 No TA in this class, you have to learn these rules to be able to deeply understand more advanced topics ;-) TA Appeasement Rules ;-) 1.When we ask you to show that something is true, you may use metatheorems. to give a formal proof 2.When we ask you to give a formal proof, it means you should write out the entire proof. no others 3.When we ask you to give a formal proof using certain rules of inference or axiom schemata, it means you should do so using only those rules of inference and axiom schemata and no others.

125 Summary on Propositional Syntax: formula, atomic formula, literal, clause Semantics: truth value, assignment, interpretation Formula satisfied by an interpretation Logical implication, entailment Satisfiability, validity, tautology, logical equivalence Deduction theorem, Contraposition Theorem Conjunctive normal form, Disjunctive Normal form, Horn form


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